Odometry is the use of data, typically acquired or otherwise sensed by various sensors equipped in a vehicle, to estimate a change in position of the vehicle over time especially when the vehicle is moving. Specifically, a few dynamic variables of the vehicle, such as linear velocities and angular velocities of the vehicle, are required for estimation of the change in position of the vehicle. The closer the estimation to an actual position of the vehicle, the more accurate the odometry is. Accurate odometry plays an important role in characterizing the vehicle's various dynamic properties, including critical data for controlling or otherwise navigating the vehicle, especially if the vehicle is unmanned or autonomously operated. For example, for a vehicle such as an automotive vehicle, a mobile robot or an airborne drone, accurate odometry may provide precise information about the vehicle's position, orientation, linear velocities and angular velocities with respect to its surrounding two-dimensional (2D) or three-dimensional (3D) environment. The dynamic information is crucial in applications such as stability control and accurate navigation of the vehicle.
Conventionally, for automotive vehicles, odometry may be achieved using wheel odometry for estimating linear velocities, and through a vehicle-embedded inertial measurement unit (IMU) for measuring angular velocities. However, wheel odometry has limited accuracy due to factors such as tire size uncertainties (e.g., user modification of tires or insufficient tire pressure) and a low wheel encoder resolution. IMU also suffers measurement errors especially in low speed maneuvers. Mobile robots and drones have conventionally been using visual odometry (via cameras equipped on the vehicle) along with IMU. However, visual odometry often suffers from an integration drift problem; namely, measurement errors in linear and/or angular velocities may integrate or otherwise accumulate over time, causing a random unbounded drift to appear in the calculated navigation position and/or altitude.
Recently, odometry based on global positioning system (GPS) and light detection and ranging (“LIDAR”, or “lidar”) technologies has been developed, as more vehicles are equipped with advanced driver assistance system (ADAS) sensors that may include a GPS or a lidar. However, GPS may not function at places where satellite reception is limited (e.g., in a tunnel), whereas lidar does not work in all weather conditions.
Moreover, many existing odometry methods are limited to providing 2D dynamic variables only (i.e., linear and angular velocities of the vehicle on a 2D plane), and not able to provide dynamic variables of the vehicle in a 3D space. The 2D variables are insufficient for control and a navigation of a vehicle traversing in a 3D space (e.g., a drone), which requires a 3D odometry capability.